Accelerating Density Functional Calculation of Adatom Adsorption on Graphene via Machine Learning.
Nan QuMo ChenMingqing LiaoYuan ChengZhonghong LaiFei ZhouJingchuan ZhuYong LiuLin ZhangPublished in: Materials (Basel, Switzerland) (2023)
Graphene has attracted significant interest due to its unique properties. Herein, we built an adsorption structure selection workflow based on a density functional theory (DFT) calculation and machine learning to provide a guide for the interfacial properties of graphene. There are two main parts in our workflow. One main part is a DFT calculation routine to generate a dataset automatically. This part includes adatom random selection, modeling adsorption structures automatically, and a calculation of adsorption properties. It provides the dataset for the second main part in our workflow, which is a machine learning model. The inputs are atomic characteristics selected by feature engineering, and the network features are optimized by a genetic algorithm. The mean percentage error of our model was below 35%. Our routine is a general DFT calculation accelerating routine, which could be applied to many other problems. An attempt on graphene/magnesium composites design was carried out. Our predicting results match well with the interfacial properties calculated by DFT. This indicated that our routine presents an option for quick-design graphene-reinforced metal matrix composites.
Keyphrases
- density functional theory
- machine learning
- aqueous solution
- molecular dynamics
- clinical practice
- artificial intelligence
- room temperature
- monte carlo
- deep learning
- carbon nanotubes
- big data
- walled carbon nanotubes
- molecular docking
- ionic liquid
- molecular dynamics simulations
- mental health
- high resolution
- gene expression
- gold nanoparticles
- reduced graphene oxide
- genome wide
- network analysis
- mass spectrometry